Recommender systems rely on large datasets of historical data and entail serious privacy
risks. A server offering recommendations as a service to a client might leak more information
than necessary regarding its recommendation model and training dataset. At the same time,
the disclosure of the client's preferences to the server is also a matter of concern. Providing
recommendations while preserving privacy in both senses is a difficult task, which often
comes into conflict with the utility of the system in terms of its recommendation-accuracy and …